BG value stats by hour
BGvalue_Summary
## time3 min mean max sd
## 1 00:00 Inf NaN -Inf NaN
## 2 01:00 78 78.0000 78 NaN
## 3 02:00 150 150.0000 150 NaN
## 4 03:00 Inf NaN -Inf NaN
## 5 04:00 105 105.0000 105 NaN
## 6 05:00 Inf NaN -Inf NaN
## 7 06:00 304 393.3333 511 106.36886
## 8 07:00 234 234.0000 234 NaN
## 9 08:00 107 191.0000 275 118.79394
## 10 09:00 198 198.0000 198 NaN
## 11 10:00 106 168.5000 231 88.38835
## 12 11:00 110 110.0000 110 NaN
## 13 12:00 65 129.6667 176 57.72637
## 14 13:00 64 127.0000 190 89.09545
## 15 14:00 Inf NaN -Inf NaN
## 16 15:00 122 122.0000 122 NaN
## 17 16:00 256 256.0000 256 NaN
## 18 17:00 71 139.0000 242 90.71384
## 19 18:00 Inf NaN -Inf NaN
## 20 19:00 79 149.0000 187 60.69596
## 21 20:00 44 138.0000 232 132.93607
## 22 21:00 161 161.0000 161 NaN
## 23 22:00 107 142.5000 178 50.20458
## 24 23:00 Inf NaN -Inf NaN
## 25 00:00 116 156.5000 197 57.27565
BG value stats by day
BGvalue_SummaryDaily
## Date2 min mean max sd
## 1 2019-10-07 71 175.4286 365 98.16289
## 2 2019-10-08 79 155.6667 304 72.36539
## 3 2019-10-09 44 143.0000 242 68.83313
## 4 2019-10-10 65 233.5714 511 144.36511
Sensor value stats by hour
Sensorvalue_Summary
## time3 min mean max sd
## 1 00:00 85 125.08333 183 33.54026
## 2 01:00 67 122.00000 193 36.93064
## 3 02:00 75 154.50000 265 56.09984
## 4 03:00 76 207.47917 320 88.43485
## 5 04:00 59 249.27083 400 114.97673
## 6 05:00 83 272.77083 400 109.70196
## 7 06:00 128 294.12500 400 85.11335
## 8 07:00 120 265.85366 395 81.21563
## 9 08:00 85 165.80435 280 54.60387
## 10 09:00 108 170.56250 214 33.70075
## 11 10:00 60 165.64583 211 53.06619
## 12 11:00 62 121.08108 197 35.23680
## 13 12:00 40 98.08333 175 50.46548
## 14 13:00 40 164.84783 284 59.46763
## 15 14:00 107 231.56250 373 77.98606
## 16 15:00 87 220.97917 362 89.78805
## 17 16:00 112 197.52083 313 52.59196
## 18 17:00 53 134.91667 245 57.98893
## 19 18:00 40 120.79167 225 57.14482
## 20 19:00 51 120.34091 197 48.90662
## 21 20:00 40 134.76190 224 58.92194
## 22 21:00 69 118.47917 201 34.68336
## 23 22:00 87 136.08333 225 34.72006
## 24 23:00 86 154.66667 199 34.26203
## 25 00:00 88 134.62500 189 39.91166
BG high (>150) count
BGHigh_Count
## time3 BG.Reading..mg.dL.
## 1 06:00 3
## 2 07:00 1
## 3 08:00 1
## 4 09:00 1
## 5 10:00 1
## 6 12:00 1
## 7 13:00 1
## 8 16:00 1
## 9 17:00 1
## 10 19:00 2
## 11 20:00 1
## 12 21:00 1
## 13 22:00 1
## 14 00:00 1
BG very high (>240) count
BGveryHigh_Count
## time3 BG.Reading..mg.dL.
## 1 06:00 3
## 2 08:00 1
## 3 16:00 1
## 4 17:00 1
BG low (<80) count
BGLow_Count
## time3 BG.Reading..mg.dL.
## 1 01:00 1
## 2 12:00 1
## 3 13:00 1
## 4 17:00 1
## 5 19:00 1
## 6 20:00 1
BG good value count (>80 and <150)
BGgood_Count
## time3 BG.Reading..mg.dL.
## 1 02:00 1
## 2 04:00 1
## 3 08:00 1
## 4 10:00 1
## 5 11:00 1
## 6 12:00 1
## 7 15:00 1
## 8 17:00 1
## 9 22:00 1
## 10 00:00 1
Temp Basal = 0 count
tempBasal_count
## NULL
Suspend basal on low count
suspendBasal_Count
## time3 Alarm
## 1 00:00 1
## 2 01:00 2
## 3 02:00 1
## 4 04:00 4
## 5 07:00 1
## 6 10:00 1
## 7 11:00 2
## 8 12:00 4
## 9 13:00 1
## 10 15:00 2
## 11 17:00 4
## 12 18:00 2
## 13 19:00 3
## 14 20:00 3
## 15 21:00 2
## 16 23:00 1
## 17 00:00 2
BG value by time and date with mean values
BGvalue_timeDaytable
## time 2019-10-07 2019-10-08 2019-10-09 2019-10-10 mean
## 1 00:00 NaN 116.0000 197 NaN 156.5000
## 2 01:00 78.0000 NaN NaN NaN 78.0000
## 3 02:00 NaN NaN 150 NaN 150.0000
## 4 03:00 NaN NaN NaN NaN NaN
## 5 04:00 NaN NaN 105 NaN 105.0000
## 6 05:00 NaN NaN NaN NaN NaN
## 7 06:00 365.0000 304.0000 NaN 511.0000 393.3333
## 8 07:00 NaN NaN 234 NaN 234.0000
## 9 08:00 NaN 107.0000 NaN 275.0000 191.0000
## 10 09:00 198.0000 NaN NaN NaN 198.0000
## 11 10:00 NaN NaN 106 231.0000 168.5000
## 12 11:00 NaN NaN NaN 110.0000 110.0000
## 13 12:00 148.0000 176.0000 NaN 65.0000 129.6667
## 14 13:00 190.0000 NaN 64 NaN 127.0000
## 15 14:00 NaN NaN NaN NaN NaN
## 16 15:00 NaN 122.0000 NaN NaN 122.0000
## 17 16:00 NaN NaN NaN 256.0000 256.0000
## 18 17:00 71.0000 104.0000 242 NaN 139.0000
## 19 18:00 NaN NaN NaN NaN NaN
## 20 19:00 NaN 79.0000 181 187.0000 149.0000
## 21 20:00 NaN 232.0000 44 NaN 138.0000
## 22 21:00 NaN 161.0000 NaN NaN 161.0000
## 23 22:00 178.0000 NaN 107 NaN 142.5000
## 24 23:00 NaN NaN NaN NaN NaN
## 25 mean 175.4286 155.6667 143 233.5714 176.9167
#heatmap
#heatmaps
executeSavedPlot(data = allData, plotName = "meanBGheat_hist", libraryPath = libraryPath,
numberDays = numberDays,changeParam.list = changeParam.list)
## Loading required package: reshape2
Sensor value by time and date with mean values
SGvalue_timeDaytable
## time 2019-10-07 2019-10-08 2019-10-09 2019-10-10 mean
## 1 00:00 141.83333 102.16667 176.41667 99.00000 129.85417
## 2 01:00 95.33333 93.58333 132.16667 166.91667 122.00000
## 3 02:00 217.66667 186.25000 121.00000 93.08333 154.50000
## 4 03:00 301.66667 280.58333 141.91667 105.75000 207.47917
## 5 04:00 367.91667 331.16667 81.50000 216.50000 249.27083
## 6 05:00 400.00000 324.58333 106.41667 260.08333 272.77083
## 7 06:00 389.00000 301.41667 162.58333 323.50000 294.12500
## 8 07:00 338.25000 187.16667 225.50000 377.80000 282.17917
## 9 08:00 190.50000 106.41667 142.91667 234.90000 168.68333
## 10 09:00 169.41667 184.00000 123.33333 205.50000 170.56250
## 11 10:00 204.75000 189.08333 78.25000 190.50000 165.64583
## 12 11:00 197.00000 155.00000 103.16667 98.75000 138.47917
## 13 12:00 NaN 160.91667 77.58333 55.75000 98.08333
## 14 13:00 189.00000 162.33333 98.33333 213.75000 165.85417
## 15 14:00 188.08333 137.75000 299.00000 301.41667 231.56250
## 16 15:00 192.75000 96.58333 333.75000 260.83333 220.97917
## 17 16:00 174.08333 138.16667 269.00000 208.83333 197.52083
## 18 17:00 101.91667 100.66667 225.16667 111.91667 134.91667
## 19 18:00 92.50000 83.25000 209.91667 97.50000 120.79167
## 20 19:00 76.91667 88.58333 168.00000 162.33333 123.95833
## 21 20:00 130.08333 177.00000 138.58333 52.00000 124.41667
## 22 21:00 88.66667 154.75000 136.16667 94.33333 118.47917
## 23 22:00 120.08333 144.08333 106.33333 173.83333 136.08333
## 24 23:00 187.41667 135.08333 173.58333 122.58333 154.66667
## 25 mean 198.03623 167.52431 159.60764 176.14028 175.32711
#heatmap
#heatmaps
executeSavedPlot(data = allData, plotName = "meanSGheat_hist", libraryPath = libraryPath,
numberDays = numberDays,changeParam.list = changeParam.list)
Interactive Plots
every 3 hours barplots
###daily barplots